Supernova search with active learning in ZTF DR3

We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 < MJD < 58483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert.Comment: 22 pages with appendix, 12 figures, 2 tables, accepted for publication in Astronomy and Astrophysic

Supernovae - a tool for observational cosmology

International audienceType Ia Supernovae (SNe) are excellent distance indicators in the Universe. Observations of distant SNe~Ia led to the discovery of the accelerating expansion of the Universe. The most recent analysis of SNe Ia indicates that considering a flat $\Lambda$CDM cosmology, the contribution of dark energy in the total density of the Universe is $\sim$70%. Cosmological parameters are estimated from the ``luminosity distance-redshift'' relation of SNe. Currently a lot of attention is paid to standardization of SNe, i.e. to increase of the accuracy of luminosity distance determination. The uncertainty on the redshift is quite often considered negligible. The redshift most often used corresponds to the one of SN host galaxy relative to CMB frame. In fact the redshift observed on the Earth also includes the contribution from the Doppler effect induced by peculiar velocities related to the Hubble flow. The existing methods to correct redshift for peculiar velocity contribution do not work in clusters of galaxies, the biggest virialized systems in the Universe where peculiar velocities can reach 1000 km~s$^{-1}$. To count for the effect of peculiar velocities in galaxy clusters we studied 145 SNe from the Nearby Supernova Factory and found 11 objects that belong to the clusters. We used the galaxy cluster redshift instead the host galaxy redshift to construct the Hubble diagram. The applied technique allowed to improve the redshift measurements and to decrease the spread on the Hubble diagram. The peculiar velocity correction of galaxies inside galaxy clusters has to be taken into account in future cosmological surveys such as LSST

The dependence of Type Ia Supernovae SALT2 light-curve parameters on host galaxy morphology

International audienceType Ia Supernovae (SNe Ia) are widely used to measure distances in the Universe. Despite the recent progress achieved in SN Ia standardization, the Hubble diagram still shows some remaining intrinsic dispersion. The remaining scatter in supernova luminosity could be due to the environmental effects that are accounted for as mass step correction in the current cosmological analyses. In this work, we compare the local and global colour (U − V), the local star formation rate, and the host stellar mass to the host galaxy morphology. The observed trends suggest that the host galaxy morphology is a relevant parameter to characterize the SN Ia environment. Therefore, we study the influence of host galaxy morphology on light-curve parameters of SNe Ia from the pantheon cosmological supernova sample. We determine the Hubble morphological type of host galaxies for a subsample of 330 SNe Ia. We confirm that the salt2 stretch parameter x_1 depends on the host morphology with the p-value ∼10^−14. The supernovae with lower stretch value are hosted mainly by elliptical and lenticular galaxies. No correlation for the salt2 colour parameter c is found. We also examine Hubble diagram residuals for supernovae hosted by ‘early-type’ and ‘late-type’ morphological groups of galaxies. The analysis reveals that the mean distance modulus residual in early-type galaxies is smaller than the one in late-type galaxies, which means that early-type galaxies contain brighter supernovae after stretch and colour corrections. However, we do not observe any difference in the residual dispersion for these two morphological groups. The obtained results are in the line with other analyses showing environmental dependence of SN Ia light-curve parameters and luminosity. We confirm the importance of including a host galaxy parameter into the standardization procedure of SNe Ia for further cosmological studies

Anomaly Detection in the Open Supernova Catalog

International audienceIn the upcoming decade, large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered transients will belong to the known classes of astronomical objects. However, it is expected that some transients will be rare or completely new events of unknown physical nature. The task of finding them can be framed as an anomaly detection problem. In this work, we perform for the first time an automated anomaly detection analysis in the photometric data of the Open Supernova Catalog (OSC), which serves as a proof of concept for the applicability of these methods to future large-scale surveys. The analysis consists of the following steps: (1) data selection from the OSC and approximation of the pre-processed data with Gaussian processes, (2) dimensionality reduction, (3) searching for outliers with the use of the isolation forest algorithm, and (4) expert analysis of the identified outliers. The pipeline returned 81 candidate anomalies, 27 (33 per cent) of which were confirmed to be from astrophysically peculiar objects. Found anomalies correspond to a selected sample of 1.4 per cent of the initial automatically identified data sample of approximately 2000 objects. Among the identified outliers we recognized superluminous supernovae, non-classical Type Ia supernovae, unusual Type II supernovae, one active galactic nucleus and one binary microlensing event. We also found that 16 anomalies classified as supernovae in the literature are likely to be quasars or stars. Our proposed pipeline represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. All code and products of this investigation are made publicly available.^

The influence of host galaxy morphology on the properties of Type Ia supernovae from the JLA compilation

International audience• We study the influence of host galaxy morphology on LC parameters of the JLA data. • The α parameter decreases from elliptical/lenticular to late-type spiral galaxies. • In an old stellar population SNe Ia are fainter after stretch and colour corrections. • The host galaxy morphology affects the residual dispersion on the Hubble diagram. • SNe Ia in late-type spiral galaxies are more homogeneous in comparison with others

The Most Interesting Anomalies Discovered in ZTF DR3 from the SNAD-III Workshop

International audienceThe search for objects with unusual astronomical properties, or anomalies, is one of the most anticipated results to be delivered by the next generation of large scale astronomical surveys. Moreover, given the volume and complexity of current data sets, machine learning algorithms will undoubtedly play an important role in this endeavor. The SNAD team is specialized in the development, adaptation and improvement of such techniques with the goal of constructing optimal anomaly detection strategies for astronomy. We present here the preliminary results from the third annual SNAD workshop (https://snad.space/2020/) that was held on-line in 2020 July